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De’ Donato, FK; Leone, M; Scortichini, M; De Sario, M; Katsouyanni,K; Lanki, T; Basagaa, X; Ballester, F; strm, C; Paldy, A; Pascal, M;Gasparrini, A; Menne, B; Michelozzi, P (2015) Changes in the Effectof Heat on Mortality in the Last 20 Years in Nine European Cities.Results from the PHASE Project. International journal of environ-mental research and public health, 12 (12). pp. 15567-83. ISSN1661-7827 DOI: https://doi.org/10.3390/ijerph121215006
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DOI: 10.3390/ijerph121215006
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Article
Changes in the Effect of Heat on Mortality in the Last20 Years in Nine European Cities. Results from thePHASE Project
Francesca K. de’ Donato 1,*, Michela Leone 1, Matteo Scortichini 1, Manuela De Sario 1,Klea Katsouyanni 2, Timo Lanki 3, Xavier Basagaña 4,5,6, Ferran Ballester 6,7, Christofer Åström 8,Anna Paldy 9, Mathilde Pascal 10, Antonio Gasparrini 11,12, Bettina Menne 13 andPaola Michelozzi 1
Received: 9 October 2015; Accepted: 1 December 2015; Published: 8 December 2015Academic Editor: Jan C. Semenza
1 Department of Epidemiology, Lazio Regional Health Service, Via di Santa Costanza 53, Rome 00198, Italy;[email protected] (M.L.); [email protected] (M.S.); [email protected] (M.D.S.);[email protected] (P.M.)
2 Department of Hygiene and Epidemiology, Medical School, University of Athens, Athens 11527, Greece;[email protected]
3 Department of Health Protection, National Institute for Health and Welfare (THL), Neulaniementie 4,PO Box 95, Kuopio 70701, Finland; [email protected]
4 Centre for Research in Environmental Epidemiology (CREAL), Doctor Aiguader 88, Barcelona 08003, Spain;[email protected]
5 Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), Doctor Aiguader 88,Barcelona 08003, Spain
6 Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP),Melchor Fernández Almagro, 3–5, Madrid 28029, Spain; [email protected]
7 FISABIO, Epidemiology and Environmental Health Joint Research Unit, Universitat Jaume I,Universitat de València, Valencia, 46020, Spain
8 Occupational and Environmental Medicine, Department of Public Health and Clinical MedicineUmeå University, Umeå 90187, Sweden; [email protected]
9 Jozsef Fodor National Center of Public Health, National Institute of Environmental Health,Department of Biological Monitoring, Gyali ut 2–6, Po Box 64, Budapest 1097, Hungary;[email protected]
10 Department of Environmental Health (DSE), Institute de Veille Sanitaire, 12, rue du Val d’Osne,Saint Maurice 94415, France; [email protected]
11 Department of Social and Environmental Health Research,London School of Hygiene and Tropical Medicine, 15–17 Tavistock Place, London WC1H 9SH, UK;[email protected]
12 Department of Medical Statistics, London School of Hygiene and Tropical Medicine, Keppel Street,London WC1E 7HT, UK
13 WHO Regional Office for Europe, European Centre for Environment and Health,Platz der Vereinten Nationen 1, Bonn D-53113, Germany; [email protected]
* Correspondence: [email protected]; Tel.: +39-06-99722174; Fax: +39-06-99722111
Abstract: The European project PHASE aims to evaluate patterns of change in thetemperature–mortality relationship and in the number of deaths attributable to heat in nine Europeancities in two periods, before and after summer 2003 (1996–2002 and 2004–2010). We performedage-specific Poisson regression models separately in the two periods, controlling for seasonality, airpollution and time trends. Distributed lag non-linear models were used to estimate the Relative Risksof daily mortality for increases in mean temperature from the 75th to 99th percentile of the summerdistribution for each city. In the recent period, a reduction in the mortality risk associated to heat wasobserved only in Athens, Rome and Paris, especially among the elderly. Furthermore, in terms ofheat-attributable mortality, 985, 787 and 623 fewer deaths were estimated, respectively, in the three
Int. J. Environ. Res. Public Health 2015, 12, 15567–15583; doi:10.3390/ijerph121215006 www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2015, 12, 15567–15583
cities. In Helsinki and Stockholm, there is a suggestion of increased heat effect. Noteworthy is that aneffect of heat was still present in the recent years in all cities, ranging from +11% to +35%. In Europe,considering the warming observed in recent decades and population ageing, effective interventionmeasures should be promoted across countries, especially targeting vulnerable subgroups of thepopulation with lower adaptive resources.
Keywords: heat; mortality; adaptation; attributable deaths; climate change; heat prevention plans
1. Introduction
Extreme temperatures are among the ten worst reported natural disasters of the past 40 yearsin terms of human lives lost globally [1]. In Europe, the death toll associated with such events ishigher than for any other natural disaster, like floods and storms [1], and the adverse effect of hightemperatures and heat waves across Europe has been extensively documented over the past decadesin several multi-city time series studies [2,3]. Vulnerability to climate extremes is determined by thetemperature–mortality relationship, which is heterogeneous across cities due to different climaticconditions, individual and population characteristics, and adaptation measures in place [4]. Localpopulation characteristics include both individual (age, gender, income, education, ethnicity and socialisolation) and contextual (population density, urban characteristics, socioeconomic, and access tohealth services) factors and may differ not only among different areas, but also over time within thesame geographical area [5–8].
Climate change predictions indicate warmer temperatures and higher frequency of extreme eventsover Europe in future decades that are likely to increase heat-related impacts [9]. Southern Europeancountries are experiencing more heat waves and higher temperatures, but northern areas are alsovulnerable as they are warming at a faster rate [4].
Adaptation measures are crucial for reducing the current and future adverse impacts of climatechange. The summer of 2003 was characterized by a very intense heat wave across most of Europewhich had a considerable impact on health; with the increase in daily deaths during heat wave dayscompared to non-heat wave days between +20 and +110% [3]. In Europe, summer 2003 has changedindividuals’ perception on heat-related health risks and has helped increase public awareness onclimate-related threats. Furthermore, after 2003 the Ministries of Health of several European countriesintroduced public health plans with the aim of improving adaptation and reducing the adverse effectsof hot weather [10,11]. These measures represent one of the few available climate adaptation strategiesin the health sector but their effectiveness has not been formally evaluated. Temporal variations in thetemperature–mortality relationship and in effect estimates have been used as an indirect evaluation ofthe potential benefits associated with the introduction of heat prevention measures. Evidence from theUnited States shows that, although mortality risk associated to heat exposure has been declining since2000 compared with previous decades, it remains significant in most cities [12–17]. A similar decliningtrend has also been observed in European countries such as France [18], the Czech Republic [19],Italy [20,21] and Germany [22].
To date, an assessment of the potential changes in the impact of heat on mortality after the 2003heat wave, considering the increase in temperatures recorded in the last decade and adaptation due tothe introduction of heat plans, has not been carried out at the European level. Within the EU co-fundedPHASE project, we estimated the temperature–mortality relationship and defined the number ofheat-attributable deaths in nine European cities in two periods, 1996–2002 and 2004–2010. The aim ofthe paper is to evaluate whether heat continues to represent an important risk factor for mortality inEuropean cities and describe the patterns of change in the elderly and in the adult population after the2003 heat wave, which somewhat changed public awareness and public health attention on the issue.
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2. Methods
2.1. Study Design and Population
The study was performed in nine European cities: Athens, Barcelona, Budapest, Helsinki, London,Paris, Rome, Stockholm and Valencia. Daily meteorological and mortality data were collected forthe years 1996–2010. The study period was restricted to 6 months from April to September, as hightemperatures during the warmer months were the exposure of interest. To investigate a possible changein the temperature–mortality relationship, two 7-year periods were compared. Specifically, period 1(P1) comprised the years 1996–2002 and period 2 (P2) years 2004–2010. Summer 2003 was chosen asthe cut off point, as it may have marked a change in the perception of health effects related to heat anda change in individual response mechanisms. Furthermore, in response to the 2003 heat wave, manyEuropean countries introduced heat prevention plans the following year. It was decided to exclude2003 from the analyses because it was an unprecedented event with record high temperatures that haddevastating effects on mortality and non-fatal health outcomes across most of Europe. Consideringthe before and after analysis, including 2003 in either two of the two 7-year periods would provide adistortion in the heat-related mortality estimates.
2.2. Mortality Data
Mortality data are represented by daily counts for all causes, excluding external causes (naturalmortality, ICD-9: 1–799), cardiovascular diseases (ICD-9: 390–459) and respiratory diseases (ICD-9:460–519). Mortality counts were classified in 4 age groups (15–64 years, 65–74 years, 75–84 years,85 plus years). It was decided to exclude children (0–14 age group) as this will be the focus of a separatestudy on the health effects of heat in the age group considering morbidity outcomes.
2.3. Environmental Data
All cities provided 3-hour meteorological data (air temperature and dew point temperature, windspeed, and barometric pressure at sea level) from the nearest airport weather station or from urbanweather monitoring stations. Partners from each city a priori selected weather data to provide for theanalyses based on data availability for the entire study period, quality of data and representativeness ofaverage urban exposure (weather station included for each city are annotated in Table 1). A preliminaryanalysis was carried out to identify the best temperature exposure metrics among mean, minimumand maximum air temperature, and mean, minimum and maximum apparent temperature. The bestfit was evaluated on the basis of the minimum cross-validated residuals and the Relative Risks (RR)from the different models considered as done by Barnett and colleagues [23]. Considering results forall cities, daily mean temperature (Tmean) was chosen as the best exposure variable (data not shown).
Air pollution data for each city were also provided by PHASE partners and were retrieved fromurban monitoring networks; daily values were calculated using a standard methodology reportedin previous European studies [24]. The maximum daily value of NO2 (lag 0–1) was chosen as anindicator of urban traffic pollution in all cities except for Barcelona where the 24-hour mean of PM10
was considered as NO2 was not available. This was included in the model as confounder variable.
2.4. Statistical Analysis
Time-series Poisson regression models were run separately in each city and in each period,in order to derive period- and city-specific temperature–mortality associations. The associations weremodeled using a distributed lag non-linear model (DLNM), a flexible way to capture the complexnon-linear and lagged dependencies of exposure–response relationships through two functionsmodeling exposure–response and lag-response relationships, respectively, and then combined ina cross-basis function [25]. Specifically, a quadratic B-spline for the exposure-response function withthree internal knots and a natural cubic B-spline for the lag-response function with an intercept andthree internal knots placed at equally spaced values in the log scale were selected. City-specific lag
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windows were considered to capture the different delay in the effect of heat in different populations.Modeling choices were tested in a sensitivity analysis changing both the type of spline, position andnumber of knots and extending the lag windows up to 40 days to consider longer delays in the effectof heat. The base model included a natural cubic B-spline of day of the year with equally spaced knotswith 6 degrees of freedom (df ) to control for seasonality, a natural cubic B-spline of time with 8 df tocontrol long-term trends, and an indicator of day of the week and holidays. To account for the addedeffect of humidity with respect to mean temperature, relative humidity (%) was also introduced in themodel as a quadratic B-spline with the same number of knots and lag as the exposure variable. Therobustness of the choice of degrees of freedom (df ) for the exposure variable, for long-term trend andfor seasonality was also checked by comparing RRs derived from different combinations of df of eachspline variable. Sensitivity analyses confirmed results from the main model.
Barometric pressure (lag 0–3) and wind speed (lag 0) were included as potential confounderson the basis of previous studies conducted in the same cities [2]. Both barometric pressure and windspeed were considered as linear terms. The maximum daily value of NO2 (lag 0–1) was also includedas a confounder in all cities.
The effect of high temperatures on mortality was expressed as the Relative Risks (RR) ofdaily mortality for increases in mean temperature from the 75th to 99th percentile of the summerperiod-specific distribution to capture the effects across a range of temperatures during the warmseason. The analyses were run by cause of death and age groups. To assess the change in the effectof heat on mortality, the RRs were calculated separately for period 1 (P1) (1996–2002) and period 2(P2) (2004–2010). To test for the statistical significance of the change in the effect the relative effectmodification (REM) index was calculated as the ratio between the period-specific relative risks asdefined in Stafoggia et al. [26]. The presence of effect modification was evaluated with a significancelevel of 0.05.
Secondly, to estimate the impact of high temperatures on mortality city-specific attributablefractions (%) of death (AF) and number of deaths (AD) were calculated for the two periods. Theseattributable measures were estimated using the methodology developed by Gasparrini and Leone(2014) within the DLNM R framework, which takes into account the additional temporal dimensionof the temperature–mortality association when providing risk estimates [27]. For each given day, theattributable fraction (AF) of mortality due to temperature was estimated by combining the risks on thegiven and previous days, according to the pre-defined lag window. The daily attributable numberof deaths (AD) was calculated by multiplying the daily AF by the daily number of deaths. The totalnumber of attributable deaths was given by the sum of the AD for all the days with temperaturesbetween the 75th and 99th percentile of mean temperature. The total heat-attributable fractionrepresents the ratio between the total AD and total number of deaths. In order to estimate empiricalconfidence intervals, Monte Carlo simulations were implemented in the model.
To estimate the potential change in the number of heat-related deaths between the two periods,we calculated the number of deaths attributable to heat considering the AF in each period by thenumber of deaths observed in period 2. This was done under the assumption that the underlyingpopulation exposed to heat does not change between periods but their response to heat has changedover time.
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Table 1. Demographic characteristics, mean temperature distribution in the two periods between April and September and heat prevention plans in nineEuropean cities.
Population Average Daily Death by Cause Mean Temperature (˝C) § Heat Prevention Plan
Cities Period Total PercentAged 65+
PercentAged 75+
Period SpecificSummer Death Rate * Total Respiratory Cardiovascular Average 75th Pctile 95th Pctile Year of Activation, Coverage
Valencia1996–2002 745,501 17.1 7.4 371.6 15.1 1.6 5.3 22.2 25.6 28.9 2004, national
2004–2010 802,273 17.4 8.5 344.1 15.1 1.7 4.8 22.0 25.9 29.7
Barcelona1997–2002 1,507,563 21.9 10.0 441.0 36.3 3.3 12.5 20.4 23.6 26.8 2004, national, regional
2004–2009 1,601,630 20.7 10.8 394.6 34.5 3.5 10.7 21.8 25.5 28.8
Athens1996–2002 3,288,193 15.7 6.3 409.5 73.6 5.5 36.1 24.3 28.4 34.0 n.a.
2004–2010 3,283,460 16.7 7.8 426.5 76.5 7.8 34.8 24.3 28.3 33.2
Rome1996–2002 2,596,061 17.9 7.3 370.2 52.5 2.6 21.1 20.5 24.0 28.2 2004, national, regional
2004–2010 2,679,363 20.5 9.1 366.6 53.7 3.1 21.3 21.1 25.0 29.4
Budapest1996–2002 1,817,370 17.0 7.2 679.4 65.9 1.9 32.7 18.1 21.7 28.2 2006, national
2004–2010 1,706,734 18.3 8.7 638.2 59.5 2.5 28.9 18.5 22.1 28.3
Paris1997–2002 6,199,901 13.1 6.1 371.7 106.8 6.7 29.9 17.0 19.9 26.7 2004, national
2004–2009 6,518,897 12.9 6.6 272.3 97.0 5.5 24.1 17.4 20.2 26.5
London1996–2002 7,163,486 12.7 6.0 376.4 147.3 23.3 57.9 15.2 17.9 24.1 2004, national
2004–2010 7,613,413 11.7 5.7 296.0 123.1 16.4 43.3 15.6 18.1 24.6
Stockholm1996–2002 1,800,947 14.4 7.4 298.7 29.4 2.2 13.4 13.3 17.1 23.3 n.a.
2004–2010 1,955,036 14.4 6.9 261.9 28.0 1.9 11.4 13.6 17.2 23.1
Helsinki1996–2002 942,492 11.4 5.0 332.5 17.1 1.5 7.4 12.6 16.9 22.7 n.a.
2004–2010 1,010,775 12.7 5.6 312.1 17.2 0.9 6.9 13.0 17.0 23.8
* (rate: per 100,000 inhabitants); § Weather stations: Valencia airport, Barcelona El Prat airport, Athens Eleftherios Venizelos airport, Rome Ciampino airport, Budapest Ferihegy airport,Paris Montsouris urban weather station, London Heathrow airport, Helsinki Vantaa airport, and Stockholm Bromma airport.
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3. Results
The cities included in the study differed by socio-demographic characteristics, climate and heatprevention programs. Population size ranged from less than one million inhabitants in Valencia andHelsinki to more than seven million in London. Population structure was slightly different betweencities, with the elderly population (65+ age group) ranging from 12% in London and Helsinki to 21%in Barcelona and Rome. In the recent period, the proportion of old and very old has increased inAthens, Budapest, Helsinki, Rome and Valencia, while in Barcelona and Paris this is only true for the75+ age group. The dataset comprised of 1,322,844 deaths that occurred between April and Septemberin the years 1996–2010 (excluding 2003). The average number of daily deaths varies considerablybetween cities, ranging from 135 deaths in London to less than 20 in Helsinki and Valencia (17 and15 deaths per day on average, respectively). Period-specific death rates are higher in Budapest andlower in Stockholm and Helsinki, while when comparing the two periods, a reduction in rates seemsto have occurred in all cities except for Athens. Cardiovascular deaths represented almost half of thetotal mortality, while respiratory deaths accounted for a smaller proportion (around 10%) (Table 1).A decline in the number of deaths was observed in all cities except for Rome and Athens.
Heat plans are present in most cities except for Athens, Stockholm and Helsinki. Table S1summarizes the results of a survey conducted within the PHASE project on the main characteristicsof the heat prevention plans in each city (www.phaseclimatehealth.eu). Some similarities are presentamong the different plans, and the common elements are early warning systems, near-real time healthsurveillance, information campaigns and prevention measures targeted to at-risk groups.
Although the study period is too short to state whether we are actually observing a change in thetrend in climatological terms, when comparing the two periods some differences are noteworthy in thecontext of our study. The local climatic characteristics differ somewhat between cities included in thestudy (Table 1, Figure 1). Cities in the Mediterranean are the warmest, while northern and continentalEuropean cities have lower average values. Figure 1 shows the mean temperature distribution bymonth in the two periods in study for each city. In the Mediterranean cities in the most recent period,temperatures have increased in July, August and September with higher extreme temperatures values.Differences were smaller in the other cities and occurring in the hottest month (July) and in September,suggesting a longer summer season. In several cities during the 20-year period considered, July seemsto have become the warmest month.
Figure 2 depicts the city-specific temperature–mortality relationship for all causes, estimated forthe periods P1 (red line) and P2 (blue line). Curves show heat effects in all cities and in both periods,with an increase in mortality as temperatures increase. Only in Helsinki the curve showed no effect ofhigh temperatures in P1. A reduction in the effect of high temperatures can be observed in the recentperiod in Paris, Rome and Athens (Figure 2, Table 2). In Barcelona, the curve shifts to the right in P2,but the slope remains essentially unchanged for high summer temperatures (Table 2). Conversely,an increase in the effect of high temperatures was observed in the northern and continental cities ofHelsinki, Stockholm and Budapest, due to higher temperatures previously not observed. In London,although there seems to be a slight shift towards warmer summer temperatures (Table 1 and Figure 1),there is no evidence of a change in the risk of mortality related to high temperatures (Figure 2, Table 2).
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Figure 1. Boxplots of mean temperature (°C) distributions by month in the warm season,
in the periods before (Period 1) and after 2003 (Period 2) in nine European cities 1996–2010.
Results by cause (Table 2) show similar patterns of change for total, respiratory and cardiovascular
causes of death in Athens, Paris and Rome, with a reduction in the effect in the second period.
In Budapest, an increase in the effect of high temperatures was detected for all causes of death
considered, although statistical significance was only reached for cardiovascular causes. In London,
although a slight reduction in the effect of heat was observed for total mortality, there seems to be an
increase in heat-related respiratory deaths. Although not statistically significant, it is interesting to note
that in Stockholm and Helsinki the increase in the effect of high temperatures was mostly attributable to
a rise in cardiovascular deaths.
Figure 1. Boxplots of mean temperature (˝C) distributions by month in the warm season, in the periodsbefore (Period 1) and after 2003 (Period 2) in nine European cities 1996–2010.
Results by cause (Table 2) show similar patterns of change for total, respiratory and cardiovascularcauses of death in Athens, Paris and Rome, with a reduction in the effect in the second period. InBudapest, an increase in the effect of high temperatures was detected for all causes of death considered,although statistical significance was only reached for cardiovascular causes. In London, although aslight reduction in the effect of heat was observed for total mortality, there seems to be an increase inheat-related respiratory deaths. Although not statistically significant, it is interesting to note that inStockholm and Helsinki the increase in the effect of high temperatures was mostly attributable to arise in cardiovascular deaths.
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Figure 2. Mean temperature–mortality relationship (with 95% confidence intervals) in the
years before 2003 (Period 1) (red line) and after 2003 (Period 2) (blue line) in Nine European
cities. The x-axes show mean temperature (°C) (city-specific lag).
When considering age groups, the temporal changes observed in the all ages population are mostly
attributable the change in heat-related deaths among the oldest age groups (75–84 and 85+ years)
(Figure 3). In Paris and Rome, a significant reduction was observed for both 75–84 and 85+ years, while
in Athens the reduction was only among the very old (85+ age group). In Helsinki, the increase in heat-
related risk was observed in the 15–64 age group, the old and very old (75–84 and 85+),
while in Stockholm, a significant increase was observed only in the 75–84 age group.
A significant effect of heat in mortality was observed also in the younger age group (15–64 years) in
several cities, which remains significant also in the recent period in Athens, Budapest and Helsinki.
Figure 2. Mean temperature–mortality relationship (with 95% confidence intervals) in the years before2003 (Period 1) (red line) and after 2003 (Period 2) (blue line) in Nine European cities. The x-axes showmean temperature (˝C) (city-specific lag).
When considering age groups, the temporal changes observed in the all ages population aremostly attributable the change in heat-related deaths among the oldest age groups (75–84 and 85+ years)(Figure 3). In Paris and Rome, a significant reduction was observed for both 75–84 and 85+ years, whilein Athens the reduction was only among the very old (85+ age group). In Helsinki, the increase inheat-related risk was observed in the 15–64 age group, the old and very old (75–84 and 85+), while inStockholm, a significant increase was observed only in the 75–84 age group.
A significant effect of heat in mortality was observed also in the younger age group (15–64 years)in several cities, which remains significant also in the recent period in Athens, Budapest and Helsinki.
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Table 2. Estimated Relative Risks (95% CI) for high temperatures and daily mortality for total,respiratory and cardiovascular causes in all age groups between the 75th and 99th percentile ofmean temperature in the periods before 2003 (Period 1) and after 2003 (Period 2). Nine European cities,1996–2010.
Cities Period All Causes Respiratory Causes Cardiovascular Causes
RR 95% Cl P Value a RR 95% Cl P Value a RR 95% Cl P Value a
Valencia1996–2002 1.11 1.04–1.17 0.81 0.53–1.33 1.12 0.85–1.46
2004–2010 1.18 1.03–1.36 0.216 1.28 0.86–1.92 0.159 1.27 0.98–1.64 0.507
Barcelona1997–2002 1.27 1.18–1.36 1.55 1.23–1.96 1.42 1.25–1.60
2004–2009 1.26 1.17–1.36 0.946 1.65 1.30–2.10 0.700 1.27 1.10–1.47 0.263
Athens1996–2002 1.63 1.53–1.75 2.10 1.72–2.56 1.79 1.64–1.96
2004–2010 1.35 1.29–1.42 <0.001 1.42 1.23–1.63 0.001 1.53 1.43–1.64 0.006
Rome1996–2002 1.53 1.45–1.61 2.04 1.65–2.53 1.72 1.59–1.86
2004–2010 1.27 1.19–1.35 <0.001 1.64 1.29–2.08 0.180 1.32 1.19–1.46 <0.001
Budapest1996–2002 1.29 1.22–1.37 1.07 0.76–1.50 0.97 0.88–1.06
2004–2010 1.33 1.27–1.40 0.451 1.52 1.24–1.87 0.082 1.44 1.35–1.53 <0.001
Paris1997–2002 1.31 1.24–1.37 1.72 1.43–2.06 1.25 1.15–1.37
2004–2009 1.11 1.06–1.17 <0.001 1.26 1.02–1.55 0.026 1.04 0.95–1.15 0.006
London1996–2002 1.20 1.16–1.25 1.26 1.15–1.39 1.23 1.15–1.30
2004–2010 1.18 1.12–1.23 0.429 1.35 1.19–1.53 0.413 1.22 1.13–1.32 0.958
Stockholm1996–2002 1.10 1.04–1.17 1.25 1.01–1.54 1.07 0.98–1.17
2004–2010 1.12 1.06–1.19 0.628 1.25 1.02–1.53 0.999 1.17 1.07–1.27 0.157
Helsinki1996–2002 1.02 0.93–1.12 1.42 1.05–1.92 1.00 0.87–1.15
2004–2010 1.24 1.14–1.35 0.003 1.06 0.68–1.65 0.287 1.18 1.02–1.35 0.111a Significance test of effect modification based on REM index.
Table 3 shows the attributable risk fraction and the number of deaths attributable to meantemperatures comprised between the 75th and 99th percentile of summer distributions in each period.In terms of impact, a significant reduction in the number of deaths was observed in period P2 inAthens, Paris and Rome (985, 787 and 623 deaths, respectively). Results for the other cities showed nosignificant changes in terms of attributable risk (AR).
Table 3. Heat attributable risk fraction and deaths in the period P1 and P2 and change in heatattributable deaths in nine European cities, 1996–2010.
P1 (Before 2003) P2 (After 2003) Change in Attributable Deaths
Cities AR% 95% Cl AttributableDeath AR% 95% Cl Attributable
Death Number of Deaths * P Value
Valencia 0.6 (0.2–1.0) 112 1.0 (0.5–1.5) 194 82 0.343
Barcelona 2.2 (1.7–2.7) 896 2.3 (2.0–2.6) 870 19 0.916
Athens 3.4 (3.0–3.8) 3200 2.4 (2.1–2.7) 2343 ´985 0.005
Rome 2.8 (2.6–3.1) 1900 1.9 (1.6–2.3) 1321 ´623 0.006
Budapest 1.5 (1.3–1.8) 1291 1.7 (1.3–2.0) 1302 136 0.597
Paris 1.6 (1.3–1.8) 1846 0.8 (0.6–1.1) 890 ´787 0.005
London 0.8 (0.6–0.9) 1486 0.7 (0.5–0.9) 1080 ´162 0.554
Stockholm 0.7 (0.3–1.0) 247 0.7 (0.4–1.0) 252 17 0.889
Helsinki 0.1 (´0.5–0.6) 14 0.9 (0.4–1.4) 202 188 0.115
* calculated assuming same population is exposed but with different AR.
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Figure 3. Estimated RRs (95% CI) for high temperatures and all cause daily mortality by age groups between the 75th and 99th percentile of
mean temperature in the periods before 2003 (Period 1) (red line) and after 2003 (Period 2) (blue line). Nine European cities, 1996–2010. *
orange boxes: significant REM index P1 and P2 effect estimate; Athens analyses by age group P1 = 1999–2002
Figure 3. Estimated RRs (95% CI) for high temperatures and all cause daily mortality by age groups between the 75th and 99th percentile of mean temperature in theperiods before 2003 (Period 1) (red line) and after 2003 (Period 2) (blue line). Nine European cities, 1996–2010. * orange boxes: significant REM index P1 and P2 effectestimate; Athens analyses by age group P1 = 1999–2002.
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4. Discussion
Findings show heat still has a considerable significant effect on mortality in the European citiesincluded in the study. Moreover, when comparing the effect estimates of heat on mortality betweenthe two periods, the study shows different patterns of change among the cities included. A significantreduction in the attributable number of deaths was observed in Athens, Rome, Paris and, marginally,in London. In the other cities, no change or an increase in the number of deaths attributable to heatwas observed. In particular, an increase in the effect of high temperatures on mortality was observed incooler cities where extreme temperatures in recent years have risen, exposing local populations to unusualsummer heat.
Different factors need to be mentioned when explaining the temporal changes in the associationbetween heat and mortality. Firstly, changes in the exposure clearly play an important role. Thewarming observed in most European cities in the last decades, as summarized in the latest IPCCreport [4], may have altered the temperature–mortality relationship, especially in areas where thelocal populations were not prepared to cope with higher temperatures. This may be the case for thenorthern European cities of London, Helsinki and Stockholm.
In Barcelona, temperatures have increased in recent years (+2 ˝C in 75th and 99th percentiles inthe period 2006–2010), and from the inspection of the temperature–mortality curve, we observe a shiftto the right of the curve in the second period, suggesting adaptation to milder summer temperatures.However, when considering the extreme temperatures there seems to be no change in heat-attributabledeaths, suggesting the impact of extreme temperatures remains unchanged or acclimatization tothese extreme exposures has not occurred. A specific study on the turning point of the curves wouldhave helped identify the temperature above which mortality increases and how these have changedover time.
Another important factor that might have influenced the temporal pattern of change in theeffect of heat on mortality are the heat-response measures undertaken at the national and local level.Heat-adaptation measures, together with improvements in infrastructures and health care servicescan have beneficial effects reducing the impact of heat on mortality [12–22]. We observed a significantdecrease in the impact of high temperatures on mortality in Rome and Paris. Results from the surveycarried out within PHASE, show that in both cities, heat plans were implemented after 2003 andinclude the core elements identified by WHO [28], such as warning systems, prevention measures,health surveillance and definition of susceptible subgroup. However, they differ somewhat in terms ofspatial coverage, management and institutions involved, reflecting local and national policy in termsof emergency response, public health and climate change strategies (Table S1). Although evidence onthe effectiveness of specific intervention measures is lacking [29,30], it is plausible that heat preventionprograms targeted to the elderly and specific susceptible groups have enhanced population awarenessand improved response to heat waves in recent years in Italy and France [18,20].
Evaluation of public health programs is challenging, particularly in the specific topic of heatprevention due to the heterogeneity of local weather patterns over time, the complexity of theheat-related prevention programs and the confounding factors that vary over time. Due to theimpossibility of conducting randomized trials in this context, before and after studies represent afeasible approach to evaluate variations in the impact of heat on mortality after implementation ofheat response plans [29,30]. These studies have also been used in other public health contexts [31].Furthermore, it is worth mentioning that research on the effectiveness of measures put in place is verysparse, but it is of great importance to identify which measures are most appropriate and effectivelytarget the limited public health resources.
In both periods, Athens was the warmest city and one of the cities with the highest effect estimates.The reduction in the impact of heat in the recent period was observed in the oldest population subgroupalso in Athens. This change has no clear attribution in terms of public health adaptation measuresor population adaptive capacity since the city has no prevention plan in place. A speculative reason
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could be that after 2003 individual awareness and adaptive capacity could have changed in responseto the great media attention in most European countries.
The use of air conditioning, as suggested by several US studies, can also influence the response toheat reducing the health effects of temperature [12,15,17,32,33]. However, a recent study suggests that inseveral areas of the US, air conditioning has reached market saturation in the last decade and thus mayplay a minor role in reducing the effect of heat [15]. To date, a similar study on European cities has notbeen carried out. Moreover, air conditioning in housing as well as in public structures is less common andheterogeneous among European countries and further research on this topic is needed to address the issue.
Previous studies identified several individual characteristics that modify the association betweenheat and mortality, in particular demographic and socio-economic factors [26,32–38]. Furthermore,it is important to consider that the pool of susceptible subgroups change over time and this aspectneeds to be considered when describing temporal variations in the impact of heat on health outcomes.Population is ageing in many European cities, thus potentially inflating the pool of subjects vulnerableto heat, as it is well known that mortality risk due to temperature increases with age and gender [5,8].Although evidence is less clear for gender differentials [5,8,26,34,36], the higher number of elderlywomen and the different susceptibility factors by gender may be associated with a higher risk amongelderly females. However, in several cities where a reduction in the effect was observed in recent years,the decrease in mortality occurred mainly in the over 75 year old population for both cardiovascularand respiratory causes suggesting the potential role of prevention plans that are focused on thesesubgroups [18,20]. Similarly, in US cities, Bobb et al. [15] found a more rapid decline in heat-relatedmortality in the elderly between 1987 and 2005. This was attributed to heat prevention programstargeting mostly this age group, or to other factors, such as a greater awareness of risks of extreme heator a shift from heat-related mortality to heat-related morbidity. It is interesting to note that, among the65–74 years olds, the reduction was minimal, or not observed, suggesting more has to be done for thisgroup in current heat prevention programs.
Considering pre-existing chronic disease, the effect of heat on cardiovascular and respiratorycauses found in our study confirms the higher vulnerability to heat in patients with these conditions(e.g., myocardial infarction survivors and COPD) as reported in previous [5,26,36,37]. This hasimportant consequences in future years as the prevalence of these diseases are growing not onlyamong the elderly but also in the younger population [39,40]. The rising risk of heat-related mortalityin some cities can be ascribed to different pre-existing disease not considered in this study such asdiabetes, neurologic and psychiatric diseases that are also on the rise among the adult population inEurope. Findings can be translated into a recommendation for public health to intensify preventionmeasures on younger population during hot weather especially towards those vulnerable due topre-existing chronic health conditions.
European countries have been facing socio-economic challenges in recent years such as theeconomic recession, rising unemployment rate, population growth in urban areas, and increasingpoverty and social exclusion [41]. The economic recession may have worsened citizens’ social andeconomic conditions thus reducing their individual adaptive capacity. Economic restrictions havealso had an impact on healthcare systems, with an abatement of expenditures for hospital equipment,personnel, prevention programs and other healthcare costs thus affecting the capacity of health systemsto respond to emergencies [10,11,42].
5. Conclusions
Climate change is recognized as the biggest global health threat of the 21st-century. Our study showsthat heat still has a considerable impact on mortality. Considering the increase in the frequency andintensity of heat waves predicted under the different climate change scenarios, it seems imperative thatpublic health adopts a central role in promoting preparedness and response to climate change amongEuropean populations. The implementation of intervention measures, specifically targeted to vulnerablesubgroups with limited adaptive capacity, will help enhance adaptation and reduce heat-related risks.
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Supplementary Material
Table S1. Main components of the Heat Plans in nine European cities included in the PHASE project.
City Year ofActivation
Level(Responsible Body)
Warning System(Model Type) Susceptible Subgroups
Information Campaign,Professional Training
Programmes
Emergency ActionsDuring Heat Waves
Health Surveillance(Indicator; Timing)
Evaluation ofPreventive Measures
Valencia 2004 national (Ministryof Health)
Yes (Maximumapparent
temperature-mortalityregression model)
elderly, chronically ill,pharmaceutical
treatments, persons withcognitive diseases
general population,susceptible groups,athletes, social and
health workers
reinforcedcommunication to
health professionals,social services, generalpopulation; additionalmeasures for targetedgroups (home visits by
the social workers,telemonitoring)
Yes (mortality; daily)
Barcelona 2004
national (Ministryof Health)
regional (Dep.t ofHealth of
Autonomous region)
Yes (Threshold modelbased on temperature(Tappmax)-mortality
relationship)
children, elderly,chronically ill, people in
pharmaceuticaltreatment, disabled,
isolated people,pregnant women
general population,susceptible groups,athletes, social and
health workers
opening coolingspaces, postponingnon-urgent surgery,
protected discharges
Yes (mortality; daily)
Athens No general population,susceptible groups No
Rome 2004
national (Ministryof Health)
regional (regionalDepartment ofHealth Care)
Yes (Threshold modelbased on temperature(Tappmax)-mortality
relationship; Air massbased model)
elderly, isolated people,chronically ill, people in
pharmaceuticaltreatments, pregnantwomen, workers in
construction, transportand mining sectors
general population,susceptible groups, social
and health workers
opening coolingspaces, postponingnon-urgent surgery,
protected discharges,mobilise community
and voluntary support;additional measuresfor targeted groups(home visits by the
family doctors)
Yes (mortality, daily;emergency visits,
weekly foremergency visits)
before and after studycomparing the health
effects of hightemperatures
(Schifano 2012)evaluation of GPs
surveillancecomparing
tempertuare effects onsruveilled/not
surveilled(Bargagli 2011)
Budapest 2006 national (NationalHealth Service)
Yes (climatologicaltempertuare
threshold model)
people in health facilities,homeless people general population
reinforced social careservices, intensified airconditioning in health
care facilities,providing water in
public areas especiallyto homeless people
Yes (mortality,ambulance calls; daily)
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Table S1. Cont.
City Year ofActivation
Level(Responsible Body)
Warning System(Model Type) Susceptible Subgroups
Information Campaign,Professional Training
Programmes
Emergency ActionsDuring Heat Waves
Health Surveillance(Indicator; Timing)
Evaluation ofPreventive Measures
Paris 2004 national (Ministryof Health)
Yes (Threshold modelbased on temperature
(tmin andTmax)-mortality
relationship)
children, elderly,chronically ill, people
with cognitive troubles,imprisoned people,
phamaceuticaltreatments, disabled,
isolated people,drug/alcohol abusers,
homeless people
general population,susceptible groups, social
and health workers
activation of free helpline, opening cooling
spaces, emergencyplans in hospitals and
retirement homes,reinforce the patrols of
the Samu Social (forhomeless people)
Yes (mortality,emergency
visits; daily)
before and after studycomparing the healtheffects of heatwaves
(Fouillet 2008)
London 2004 national (NationalHealth Service)
Yes (climatologicaltempertaure thresholdmodels-Tmax Tmin)
children, elderly,chronically ill, disabled,drug/alcohol abusers
general population,susceptible groups,
Muslims duringRamadan, social and
health workers
media alerts, supportorganisations to reduce
unnecessary travel,review safety of public
events, mobilisecommunity and
voluntary support
Yes (mortality,emergency visits andNHS 111 calls; weekly,
during heatwaves daily)
Stockholm general population
Helsinki general population,health workers
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Acknowledgments: This publication arises from the PHASE project (N. 20101103), which was co-funded by theEuropean Union, in the framework of the Health Programme. Antonio Gasparrini was supported by the UKMedical Research Council (grant ID: MR/M022625/1).
Author Contributions: Michela Leone, Francesca K. de’ Donato, Klea Katsouyanni, Timo Lanki, Xavier Basagaña,Ferran Ballester, Christofer Åström, Anna Paldy, Mathilde Pascal and Antonio Gasparrini provided datasets fromeach participating city. Paola Michelozzi, Michela Leone and Francesca K. de’ Donato defined the study design.Michela Leone and Matteo Scortichini carried out the statistical analyses. Francesca K. de’ Donato, Manuela DeSario, Paola Michelozzi and Michela Leone wrote the paper. All authors contributed to discussion on the studydesign and reviewing drafts of the paper.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. World Meteorological Organization (WMO). Atlas of Mortality and Economic Losses from Weather, Climate andWater Extremes (1970–2012); World Meteorological Organization: Geneva, Switzerland, 2014.
2. Baccini, M.; Biggeri, A.; Accetta, G.; Kosatsky, T.; Katsouyanni, K.; Analitis, A.; Anderson, H.R.; Bisanti, L.;D’Ippoliti, D.; Danova, J.; et al. Heat effects on mortality in 15 European cities. Epidemiology 2008, 19, 711–719.[CrossRef] [PubMed]
3. D’Ippoliti, D.; Michelozzi, P.; Marino, C.; de’Donato, F.; Menne, B.; Katsouyanni, K.; Ursula, K.; Antonis, A.;Mercedes, M.-R.; Anna, P.; et al. The impact of heat waves on mortality in 9 European cities: Results from theEuroHEAT project. Environ. Health 2010, 9. [CrossRef] [PubMed]
4. Kovats, R.S.; Valentini, R.; Bouwer, L.M.; Georgopoulou, E.; Jacob, D.; Martin, E.; Rounsevell, M.;Soussana, J.-F. Europe. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: RegionalAspects; Barros, V.R., Field, C.B., Dokken, D.J., Mastrandrea, M.D., Mach, K.J., Bilir, T.E., Chatterjee, M.,Ebi, K.L., Estrada, Y.O., Genova, R.C., et al, Eds.; Cambridge University Press: Cambridge, UK; New York,NY, USA, 2014.
5. Basu, R. High ambient temperature and mortality: A review of epidemiologic studies from 2001 to 2008.Environ. Health 2009, 8. [CrossRef] [PubMed]
6. Anderson, B.G.; Bell, M.L. Weather-related mortality: How heat, cold, and heat waves affect mortality in theUnited States. Epidemiology 2009, 20, 205–213. [CrossRef] [PubMed]
7. Hajat, S.; Kosatky, T. Heat-related mortality: A review and exploration of heterogeneity. J. Epidemiol.Commun. Health 2010, 64, 753–760. [CrossRef] [PubMed]
8. Benmarhnia, T.; Deguen, S.; Kaufman, J.S.; Smargiassi, A. Vulnerability to heat-related mortality. Epidemiology2015, 26, 781–793. [CrossRef] [PubMed]
9. Ballester, J.; Robine, J.-M.; Herrmann, F.R.; Rodó, X. Long-term projections and acclimatization scenarios oftemperature-related mortality in Europe. Nature Commun. 2011, 2. [CrossRef] [PubMed]
10. Lowe, D.; Ebi, K.L.; Forsberg, B. Heatwave early warning systems and adaptation advice to reduce humanhealth consequences of heatwaves. Int. J. Environ. Res. Public Health 2011, 8, 4623–4648. [CrossRef] [PubMed]
11. Bittner, M.-I.; Matthies, E.F.; Dalbokova, D.; Menne, B. Are European countries prepared for the next bigheat-wave? European J. Public Health 2014, 24, 615–619. [CrossRef] [PubMed]
12. Davis, R.E.; Knappenberger, P.C.; Novicoff, W.M.; Michaels, P.J. Decadal changes in summer mortality in U.S.cities. Int. J. Biometeorol. 2013, 47, 166–175.
13. Barnett, A.G. Temperature and cardiovascular deaths in the US elderly: Changes over time. Epidemiology2007, 18, 369–372. [CrossRef] [PubMed]
14. Sheridan, S.C.; Kalkstein, A.J.; Kalkstein, L.S. Trends in heat-related mortality in the United States, 1975–2004.Natur. Hazards 2009, 50, 145–160. [CrossRef]
15. Bobb, J.F.; Peng, R.D.; Bell, M.L.; Dominici, F. Heat-related mortality and adaptation to heat in the UnitedStates. Environ. Health Perspect. 2014. [CrossRef] [PubMed]
16. Gasparrini, A.; Guo, Y.; Hashizume, M.; Kinney, P.L.; Petkova, E.P.; Lavigne, E.; Zanobetti, A.; Schwartz, J.D.;Tobias, A.; Leone, M.; et al. Temporal variation in heat-mortality associations: A multicountry study.Environ. Health Perspect. 2015. [CrossRef] [PubMed]
17. Nordio, F.; Zanobetti, A.; Colicino, E.; Kloog, I.; Schwartz, J. Changing patterns of the temperature–mortalityassociation by time and location in the US, and implications for climate change. Environ. Int. 2015, 81, 80–86.[PubMed]
15581
Int. J. Environ. Res. Public Health 2015, 12, 15567–15583
18. Fouillet, A.; Rey, G.; Wagner, V.; Laaidi, K.; Empereur-Bissonnet, P.; Le Tertre, A.; Frayssinet, P.;Bessemoulin, P.; Laurent, F.; De Crouy-Chanel, P.; et al. Has the impact of heat waves on mortality changedin France since the European heat wave of summer 2003? A study of the 2006 heat wave. Int. J. Epidemiol.2008, 37, 309–317. [CrossRef] [PubMed]
19. Kyselý, J.; Plavcová, E. Declining impacts of hot spells on mortality in the Czech Republic, 1986–2009:Adaptation to climate change? Climatic Change 2012, 113, 437–453. [CrossRef]
20. Schifano, P.; Leone, M.; De Sario, M.; de’Donato, F.; Bargagli, A.; D’Ippoliti, D.; Marino, C.; Michelozzi, P.Changes in the effects of heat on mortality among the elderly from 1998–2010: Results from a multicentertime series study in Italy. Environ. Health 2012, 11. [CrossRef] [PubMed]
21. Morabito, M.; Profili, F.; Crisci, A.; Francesconi, P.; Gensini, G.F.; Orlandini, S. Heat-related mortality in theFlorentine area (Italy) before and after the exceptional 2003 heat wave in Europe: An improved public healthresponse? Int. J. Biometeorol. 2012, 56, 801–810. [CrossRef] [PubMed]
22. Heudorf, U.; Schade, M. Heat waves and mortality in Frankfurt am Main, Germany, 2003–2013: What effectdo heat-health action plans and the heat warning system have? Zeitschrift Für Gerontologie Und Geriatrie 2014,47, 475–482. [CrossRef] [PubMed]
23. Barnett, A.G.; Tong, S.; Clements, A.C.A. What measure of temperature is the best predictor of mortality?Environ. Res. 2010, 110, 604–611. [CrossRef] [PubMed]
24. Katsouyanni, K.; Schwartz, J.; Spix, C.; Touloumi, G.; Zmirou, D.; Zanobetti, A.; Wojtyniak, B.; Vonk, J.M.;Tobias, A.; Pönkä, A. Short-term effects of air pollution on health: A European approach using epidemiologictime series data: The APHEA protocol. J. Epidemiol. Commun. Health 1996, 50 (Suppl. 1), S12–S18. [CrossRef]
25. Gasparrini, A.; Armstrong, B.; Kenward, M.G. Distributed lag non-linear models. Stat. Med. 2010, 29,2224–2234. [CrossRef] [PubMed]
26. Stafoggia, M.; Forastiere, F.; Agostini, D.; Biggeri, A.; Bisanti, L.; Cadum, E.; Caranci, N.; de’Donato, F.;De Lisio, S.; De Maria, M.; et al. Vulnerability to heat-related mortality: A multicity, population-based,case-crossover analysis. Epidemiology 2006, 17, 315–323. [CrossRef] [PubMed]
27. Gasparrini, A.; Leone, M. Attributable risk from distributed lag models. BMC Med. Res. Methodol. 2014, 14.[CrossRef] [PubMed]
28. WHO Regional Office for Europe. Heat-Health Action Plans Guidance; World Health Organization:Copenhagen, Denmark, 2008.
29. Boeckmann, M.; Rohn, I. Is planned adaptation to heat reducing heat-related mortality and illness? Asystematic review. BMC Public Health 2014, 14. [CrossRef] [PubMed]
30. Toloo, G.; FitzGerald, G.; Aitken, P.; Verrall, K.; Tong, S. Evaluating the effectiveness of heat warning systems:Systematic review of epidemiological evidence. Int. J. Public Health 2013, 58, 667–681. [CrossRef] [PubMed]
31. Stallings-Smith, S.; Zeka, A.; Goodman, P.; Kabir, Z.; Clancy, L. Reductions in cardiovascular, cerebrovascular,and respiratory mortality following the national Irish smoking ban: Interrupted time-series analysis.PLoS ONE 2013, 8. [CrossRef]
32. Medina-Ramón, M.; Schwartz, J. Temperature, temperature extremes, and mortality: A study ofacclimatisation and effect modification in 50 US cities. Occup. Environ. Med. 2007, 64, 827–833. [CrossRef][PubMed]
33. Ostro, B.; Rauch, S.; Green, R.; Malig, B.; Basu, R. The Effects of temperature and use of air conditioning onhospitalizations. Am. J. Epidemiol. 2010, 172, 1053–1061. [CrossRef] [PubMed]
34. O’Neill, M.S.; Zanobetti, A.; Schwartz, J. Modifiers of the temperature and mortality association in seven UScities. Am. J. Epidemiol. 2003, 157, 1074–1082. [CrossRef] [PubMed]
35. Zanobetti, A.; O’Neill, M.S.; Gronlund, C.J.; Schwartz, J.D. Susceptibility to mortality in weather extremes:Effect modification by personal and small-area characteristics. Epidemiology 2013, 24, 809–819. [CrossRef][PubMed]
36. Schifano, P.; Cappai, G.; De Sario, M.; Michelozzi, P.; Marino, C.; Bargagli, A.; Perucci, C.A. Susceptibilityto heat wave-related mortality: A follow-up study of a cohort of elderly in Rome. Environ. Health 2009, 8.[CrossRef] [PubMed]
37. Oudin, A.D.; Schifano, P.; Asta, F.; Lallo, A.; Michelozzi, P.; Rocklöv, J.; Forsberg, B. The effect of heatwaves on mortality in susceptible groups: A cohort study of a Mediterranean and a northern European City.Environ. Health 2015, 14. [CrossRef]
15582
Int. J. Environ. Res. Public Health 2015, 12, 15567–15583
38. Gronlund, C.J.; Berrocal, V.J.; White-Newsome, J.L.; Conlon, K.C.; O’Neill, M.S. Vulnerability to extremeheat by socio-demographic characteristics and area green space among the elderly in Michigan, 1990–2007.Environ. Res. 2015, 136, 449–461. [CrossRef] [PubMed]
39. O’Leary, J.M.; Siddiqi, O.K.; de Ferranti, S.; Landzberg, M.J.; Opotowsky, A.R. The changing demographicsof congenital heart disease hospitalizations in the United States, 1998 through 2010. JAMA 2013, 309, 984–986.[CrossRef] [PubMed]
40. Price, D.B.; Yawn, B.P.; Jones, R.C.M. Improving the differential diagnosis of chronic obstructive pulmonarydisease in primary care. Mayo Clinic Proc. 2010, 85, 1122–1129. [CrossRef] [PubMed]
41. European Union. Eurostat Regional Yearbook 2014. Available online: http://ec.europa.eu/eurostat/documents/3217494/5786129/KS-HA-14-001-00-EN.PDF (accessed on 2 December 2015).
42. The Crisis, Hospitals and Healthcare. HOPE Report. Available online: http://www.hope.be/05eventsandpublications/docpublications/86_crisis/86_HOPE-The_Crisis_Hospitals_Healthcare_April_2011.pdf(accessed on 2 December 2015).
© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons by Attribution(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
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